IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1255-d745168.html
   My bibliography  Save this article

An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

Author

Listed:
  • Ziyu Gu

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Shuwei Pang

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Wenxiang Zhou

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yuchen Li

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Qiuhong Li

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

Suggested Citation

  • Ziyu Gu & Shuwei Pang & Wenxiang Zhou & Yuchen Li & Qiuhong Li, 2022. "An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines," Energies, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1255-:d:745168
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1255/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1255/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Miao Yu & Xianqiang Yang & Xinpeng Liu, 2021. "LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(7), pages 1452-1465, May.
    2. Qianjing Chen & Jinquan Huang & Muxuan Pan & Feng Lu, 2019. "A Novel Real-Time Mechanism Modeling Approach for Turbofan Engine," Energies, MDPI, vol. 12(19), pages 1-18, October.
    3. Nannan Gu & Xi Wang & Feiqiang Lin, 2019. "Design of Disturbance Extended State Observer (D-ESO)-Based Constrained Full-State Model Predictive Controller for the Integrated Turbo-Shaft Engine/Rotor System," Energies, MDPI, vol. 12(23), pages 1-24, November.
    4. Jinfu Liu & Yujia Ma & Linhai Zhu & Hui Zhao & Huanpeng Liu & Daren Yu, 2020. "Improved Gain Scheduling Control and Its Application to Aero-Engine LPV Synthesis," Energies, MDPI, vol. 13(22), pages 1-18, November.
    5. Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Corrected Equilibrium Manifold Expansion Model for Gas Turbine System Simulation and Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    6. Chengkun Lv & Ziao Wang & Lei Dai & Hao Liu & Juntao Chang & Daren Yu, 2021. "Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model," Energies, MDPI, vol. 14(19), pages 1-24, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongyi Chen & Qiuhong Li & Shuwei Pang & Wenxiang Zhou, 2022. "A State Space Modeling Method for Aero-Engine Based on AFOS-ELM," Energies, MDPI, vol. 15(11), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Penghui Sun & Xi Wang & Shubo Yang & Bei Yang & Huairong Chen & Bin Wang, 2021. "Bumpless Transfer of Uncertain Switched System and Its Application to Turbofan Engines," Energies, MDPI, vol. 14(16), pages 1-21, August.
    2. Michal Frivaldsky, 2021. "Advanced Perspectives for Modeling Simulation and Control of Power Electronic Systems," Energies, MDPI, vol. 14(23), pages 1-2, December.
    3. Lv, Chengkun & Lan, Zhu & Wang, Ziao & Chang, Juntao & Yu, Daren, 2024. "Intelligent ammonia precooling control for TBCC mode transition based on neural network improved equilibrium manifold expansion model," Energy, Elsevier, vol. 288(C).
    4. Lingfei Xiao & Yushuo Tan & Robert R. Sattarov & Ye Wei, 2024. "Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines," Energies, MDPI, vol. 17(18), pages 1-16, September.
    5. Zeyang Zhou & Jun Huang, 2020. "Study of the Radar Cross-Section of Turbofan Engine with Biaxial Multirotor Based on Dynamic Scattering Method," Energies, MDPI, vol. 13(21), pages 1-20, November.
    6. Chengkun Lv & Ziao Wang & Lei Dai & Hao Liu & Juntao Chang & Daren Yu, 2021. "Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model," Energies, MDPI, vol. 14(19), pages 1-24, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1255-:d:745168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.